Template creation method and image processor therefor

ABSTRACT

An object of the present invention is to maintain the ease with which a template is created based on design data without acquiring an actual image, which is achieved by providing the template with equivalent information contained by a template used for image recognition that involves same-type image comparison, and to improve image recognition performance by increasing the matching rate between a template and an actual image. 
     To achieve the above object, the present invention provides a method, apparatus, and program for creating based on design data a template that is used for image recognition, wherein luminance information is set for each area in the template based on the material information of the region defined by the template. Specifically, the luminance information is set based on at least one of information from among the above material information, the pattern size information of a pattern arranged in the region defined by the template, the setup conditions of an imaging apparatus, the layer information of the region defined by the template, and the outline information of a pattern.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method for creating templates thatare used to detect a particular position in a semiconductor device andto an image processor and a program therefore and, in particular, to atemplate creation method based on the design data of a semiconductordevice or the like.

2. Description of the Related Art

Conventional semiconductor measurement devices recognize an image bycomparison between SEM (scanning electron microscope) images, between anSEM image and an OM (optical microscope) image, or between OM images. Incontrast, some of recent image recognition technologies utilize designdata to recognize an image as in comparison between design data and anSEM image or between design data and an OM image.

For example, Japanese Unexamined Patent Application Publication No.2002-328015 (Patent Document 1, corresponding to U.S. Pat. No.7,026,615) discloses a technique for creating based on design data (CADdata) a template image to be used for pattern matching in a SEM image.This technique is designed to smooth the design data so as to create atemplate close to an actual image.

SUMMARY OF THE INVENTION

In image recognition involving comparison between the two images of thesame type, the success rate of image recognition can be increased withthe use of pattern edge information and contrast information. In imagerecognition with the use of design data, however, the pattern edgeinformation can be utilized, but the contrast information cannot. Thus,a problem with the latter image recognition is that the success rate ofimage recognition and its performance cannot be improved beyond acertain level, in comparison with the former image recognition involvingcomparison between the two images of the same type (e.g., between SEMimages or between OM images).

The technique disclosed in Patent Document 1 is designed to supplementthe shapes of pattern edge portions in design data so as to reduce thedifference in pattern edge shape between the design data and an SEMimage, whereby the matching rate between the two can be increaseddrastically. However, Patent Document 1 is not designed to use thecontrast information, which is not present in design data, so as toincrease its image recognition performance up to the level of the imagerecognition that involves same-type image comparison.

An object of the present invention is thus to maintain the ease withwhich a template is created based on design data without acquiring anactual image, which is achieved by providing the template withequivalent information contained by a template used for imagerecognition that involves same-type image comparison, and to improveimage recognition performance by increasing the matching rate between atemplate and an actual image.

To achieve the above object, the present invention provides a method,apparatus, and program for creating based on design data a template thatis used for image recognition, wherein luminance information is set foreach area in the template based on the material information of theregion defined by the template. Specifically, the luminance informationis set based on at least one of information from among the abovematerial information, the pattern size information of a pattern arrangedin the region defined by the template, the setup conditions of animaging apparatus, the layer information of the region defined by thetemplate, and the outline information of a pattern.

The above configuration can improve image recognition performance usingon a template that is created based on design data.

BRIEF DESCRIPTION OF THE DRAWINGS

Other objects and advantages of the invention will become apparent fromthe following description of embodiments with reference to theaccompanying drawings in which:

FIG. 1 is a schematic diagram of a scanning electron microscope;

FIG. 2 is a schematic configuration diagram of a system including theSEM;

FIGS. 3A to 3D show design data for each layer of a wafer;

FIG. 4A shows a template used for image recognition when design data foreach layer is simply overlapped, FIG. 4B showing its corresponding OMimage;

FIG. 5A shows an exemplary template used for image recognition in whicheach layer is provided with luminance information, FIG. 5B showing itscorresponding OM image;

FIGS. 6A to 6D show the light reflection properties of various patterns;

FIGS. 7A to 7D show an example of a gray-level-adjusted template usedfor image recognition;

FIGS. 8A to 8C show the yield of secondary electrons that variesdepending on material types and the setup conditions of the scanningelectron microscope; and

FIG. 9 is a flow chart showing the process of creating a template usedfor image recognition.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Described hereinafter with reference to the accompanying drawings is atemplate creation method with the use of design data, an embodiment ofthe invention.

FIG. 1 shows one of the exemplary configurations of an SEM. In the SEM,a primary electron beam 104 is applied to a cathode 101 and a firstanode 102 and accelerated toward the subsequent-stage lens system by avoltage Vacc (acceleration voltage) applied to a second anode 103.

The primary electron beam 104 is focused as a tiny spot onto a sample(synonymous with “wafer” or “semiconductor device”) 107 by a convergentlens 105 and an objective lens 106, both controlled by a lens controlpower supply 114. The focused primary electron beam 104 is thentwo-dimensionally scanned across the sample 107 by two-stage deflectingcoils 108. Scanning signals for the deflecting coils 108 are controlledby a deflection control device 109 based on desired imagemagnifications. By the primary electron beam 104 being scanned acrossthe sample 107, secondary electrons 110 are generated from the sample107. These secondary electrons 110 are detected by a secondary electrondetector 111. The secondary electron information obtained by thesecondary electron detector 111 is then amplified by an amplifier 112and displayed on a CRT 113. In the apparatus shown in FIG. 1, the samplepattern information thus displayed on the CRT 113 is used toautomatically measure patterns on the sample 107.

FIG. 2 illustrates the configuration of a system that incorporates theSEM of FIG. 1. The SEM, designated 201 in FIG. 2, connects to a loadlock chamber 202, a mini environment 203, a control device 204, and adesign data management device 205. The load lock chamber 202 has avacuum pump connected thereto so that the pump preliminarily evacuatesthe vacuum chamber of the SEM upon sample loading. Inside the minienvironment 203 is an optical microscope, and images obtained with thisoptical microscope are used to perform sample pre-alignment. The samplepre-alignment is finely adjusted by an adjusting mechanism (not shown)based on image recognition of the optical microscope so that the sampleis positioned at a particular position. The control device 204 is tocontrol the optical elements of the SEM 201, the preliminary evacuationof the SEM vacuum chamber performed in the load lock chamber 202, theoptical microscope inside the mini environment 203, and the like. Thedesign data management device 205, part of the image processor accordingto the invention, stores on its storage medium (not shown) design dataof samples to be measured or observed by the SEM. This storage mediumalso stores a program necessary for creating, based on the design data,templates to be used for pattern matching. As described above, thesystem of FIG. 2 includes two imaging apparatuses: the SEM 201 and theoptical microscope.

Described next is an exemplary program algorithm for template creationby the design data management device 205, which is connected to theabove two imaging apparatuses. Although, in FIG. 2, the SEM 201, thecontrol device 204 that controls the SEM 201, and the design datamanagement device 205 are structurally different, it should be notedthat the present invention is not limited to that configuration. Forexample, the control device 204, instead of the design data managementdevice 205, may execute the template creation program by its operationalfunctions.

FIG. 3 are examples of design data of a certain position in asemiconductor device. FIGS. 3A to 3C show the vertically same sampleregion with respect to the surface of the sample and each show designdata of different layers of the sample. FIG. 3A is data of the uppermostlayer obtained at the time of SEM measurement, FIG. 3B data of a layerbelow it. FIG. 3C is design data of a dummy layer, FIG. 3D design dataobtained by overlapping the above three layers of design data.

Unlike design data, OM images (those obtained by an optical microscope)include underlayer information and contrast information, which is notrepresented by design data. When a template is to be created based ondesign data, appropriately capturing such information enables creationof a template close to an OM image. As a result, the matching ratebetween the two can be improved drastically. In the present embodiment,the template luminance level is set according to the layers or elementmaterials of a semiconductor device, thereby creating a template closeto an actual image.

Specifically, the luminance levels for various positions in a templateare determined based on the following rules. 1) Because reflectancediffers among positions in a semiconductor device, for example, betweenan upper layer and a lower layer, the template luminance level is sethigh for high reflectance positions and low for relatively lowreflectance positions. 2) Because reflectance also differs amongmaterials of a semiconductor device, the template luminance level is setaccording to the reflectance levels of the materials. Below is anexample of this template luminance level setting.

FIG. 4A is an image recognition template based on the design data ofFIG. 3D, and FIG. 4B is its corresponding OM image. Note that if thedesign data of FIG. 3A to 3C are used to create their correspondingimage recognition templates, the probability of image recognitionfailure increases due to a great information amount difference betweenthe design data of FIG. 3A to 3C and the actual OM image of FIG. 4B. Forthis reason, the composite data of FIG. 3D is used to create a template.

Since design data does not have such contrast information as included byan OM image, it expresses contrast by image binarization (bydistinguishing between regions with signals and regions withoutsignals). This results in such an image recognition template in blackand white as shown in FIG. 4A. As can be seen, there is a significantinformation difference between the template of FIG. 4A, in whichdifferent layers of the semiconductor device are indistinguishable, andthe actual OM image of FIG. 4B, suggesting a high probability of imagerecognition failure.

To solve this problem, different luminance levels (also called gradationlevels or gray levels) are set for different design data layers, asshown in FIG. 5A. In the figure, an uppermost layer 400 of a sample hasa high luminance level, a dummy layer 401 an intermediate luminancelevel, a lower layer 402 a low luminance level. Further, a background403 has an intermediate luminance level. By thus setting an appropriategray level for each layer, the information amount necessary for imagerecognition can be optimized. In addition, gray levels are alsodetermined based on semiconductor element materials because theirluminance levels are different when imaged.

The gray level setting is performed based on the following rules, asshown in FIG. 6. First of all, a metal layer and a quartz layer differin reflectance, as shown in FIG. 6A: the reflectance of the metal layeris greater than that of the quartz layer. Thus, gray levels are set soas to reflect this relation. Further, as shown in FIG. 6B, the lightreflection intensity of an upper layer is greater than that of a lowerlayer; thus, gray levels are also set so as to reflect this relation.Furthermore, as shown in FIG. 6C, a large pattern on a sample reflectsthe better part of incident light and thus has a higher light reflectionintensity than a small pattern. Accordingly, gray levels are also set soas to reflect this relation, the light reflection intensity of a largepattern>the light reflection intensity of a small pattern. Moreover, theoutlines of design data (sample patterns) are set black because theycorrespond to slanted portions of patterns as shown by a pattern edge501 of FIG. 6D.

Applying gray colors to design data in this manner provides betterinformation for image recognition, also improving the success rate ofimage recognition. In addition, the contrast information that had notbeen available with design data can also be utilized, thus increasingthe diversity of applicable image recognition algorithms.

The above-mentioned gray level setting rules can also be combined inorder to finalize a gray level. For example, a gray level can bedetermined by parameterizing 1) a pattern material, 2) the layerposition where the pattern exist at the time of measurement, and 3) thepattern size and by taking these parameters collectively intoconsideration. Specifically, a gray level can be finalized by using suchparameters as coefficients for the following formula, for example: graylevel initial setup value×An (coefficient defined according to materialtypes)×Bn (coefficient defined according to layers)×Cn (coefficientdefined according to pattern sizes), . . . , etc. In addition, thepattern size coefficient can be fixed (for example, to 1) if the sizesof patterns across a sample are uniform enough to be negligible.Moreover, the types of wafers or the like can also be parameterized touse them as coefficients.

The above gray level setting conditions are contained in design data ofa semiconductor device. By examining the uppermost layer information atthe time of sample measurement and image-acquisition positionalinformation, gray levels can be derived with ease, and this process canalso be put into an algorithm easily. In addition, instead ofcalculating gray levels each time the necessity arises, therelationships between the above parameters and gray levels can be putinto a table in advance so that gray levels can be assigned easilywithout such calculation.

FIG. 9 is a flowchart illustrating the process flow of obtaining, basedon design data, gray levels for a template image that is used for OMimage recognition. First, in S901, a sample region to be registered as atemplate is selected from among the design data. In S902, apredetermined gray level is set for the background region in theselected sample region. The background gray level can be set as desired,but should preferably set at an intermediate gray level, which isneither too bright nor too dark. Next in S903, a low gray level(equivalent to zero gradation value; almost black) is set for theoutlines (boundaries) of the patterns in the selected sample region.Finally in S904, gray levels are set for the pattern areas surrounded bythe outlines in accordance with the above-mentioned gray level settingrules.

By following the above gray-level setting process to set gray levels fora template, an image recognition template close to an actual image canbe created.

Described next is a gray level setting method for a template that isused for SEM image recognition. In contrast to OM images, SEM imagesvary in their luminance level depending on the efficiency in detectingsecondary electrons or backscattered electrons. Thus, gray levels aredetermined based on this electron detection efficiency.

The yield of secondary electrons from a sample is determined by 1) thelanding energy of an incident electron beam (acceleration voltage withwhich the electron beam reaches the sample), 2) sample material, and 3)pattern edges on the sample. In addition, because a probe current forthe electron beam also contributes in brightening SEM images, thiscurrent is also considered, when necessary, in determining gray levels.Further, when the acceleration voltage for the electron beam is high,this electron beam often reaches an underlayer region of the sample; inother words, when the acceleration voltage is greater than apredetermined value, underlayer patterns of the sample become visiblethat would otherwise be invisible with a low acceleration voltage. Thus,this can also be considered, if necessary, upon gray leveldetermination.

FIG. 8A is a graph illustrating the relationship between the landingenergy of an incident electron beam onto a sample and the yield ofsecondary electrons from the sample. As can be seen in the figure, theyield of secondary electrons varies significantly according to theenergy magnitude of the electron beam onto the sample. In other words,the sample luminance significantly changes according to the beam energy.For the purpose of setting appropriate gray levels according to thelevel of this sample luminance, the landing energy of electron beams isthus taken into consideration. The landing energy (LE) of an electronbeam onto a sample takes the same value as the voltage (Vacc) that isapplied to acceleration electrodes provided in an electron microscope.In SEMs that control the landing energy by applying a negative voltage(Vr) to a sample, the landing energy is obtained by subtracting thenegative voltage from the acceleration voltage (LE=Vacc−Vr).

FIG. 8B is a table illustrating the relationship between sample typesand the yield of secondary electrons. With that table, gray levels areset according to sample types. Further, as shown in FIG. 8C, secondaryelectrons are generated more from the edge portions of a pattern, andthe edge portions thus tend to become brighter than the rest portions.Therefore, gray levels are preferably set such that the edge portionsare made still brighter. Note that all of the above conditions need notbe used: gray levels can be set using at least one of the conditions.

Explained next with reference to FIG. 7 are a gray-level-adjustedtemplate and a template that is created based solely on design data.FIG. 7A is typical design data, and FIG. 7B is a template that iscreated based on the design data. FIG. 7C is part of an SEM image thatis to be recognized with the template of FIG. 7B. FIG. 7D is a templateobtained by adjusting the gray levels of the template of FIG. 7B.obviously, the gray-level-adjusted template of FIG. 7D is closer to theSEM image of FIG. 7C than the template of FIG. 7B that is created basedsolely on the design data.

In the gray-level-adjusted template of FIG. 7D, an outline 600corresponds to an edge portion where secondary electrons are generatedmore. Thus, the gray level for that portion is set high (in thisexample, the highest gradation level; almost white). Portions 601 and602 are different in their material; thus, their gray levels are setaccording to material types pre-registered.

Steps S911 to S914 in FIG. 9 is a flowchart illustrating the processflow of obtaining, in accordance with the gray level setting conditionsexplained with FIGS. 8A to 8C, gray levels for a template image that isused for SEM image recognition. First, in S911, a sample region to beregistered as a template is selected from among design data. In S912, apredetermined gray level is set for the background region in theselected sample region. The background gray level is set according tothe yield of secondary electrons. Next in S913, a high gray level(white) is set for the outlines (boundaries) of the patterns in theselected sample region. As mentioned above, the outlines (boundaries) ofa pattern correspond to the edge portions of that pattern on an actualdevice in which portions the yield of secondary electrons is high. Thus,a relatively high gray level is set in comparison with the restportions. Finally in S914, gray levels are set for the pattern areassurrounded by the outlines in accordance with the yield of secondaryelectrons and material types. In this gray level setting for a templateused for SEM image recognition, such parameters as mentioned earlier canalso be used as coefficients and put into a table. In accordance withsuch rules as mentioned above, gray levels are set for each pixel or foreach region defined by pattern outlines.

In accordance with the above configuration, a template image close to anactual image can be created based on the design data of a semiconductordevice or on the setup conditions of microscopes without acquiring an OMimage or SEM image. Further, when design data to which gray levelinformation is added is subjected to differential processing, imagerecognition can be performed based on pattern edge information.

While the invention has been described in its preferred embodiments, itis to be understood that the words which have been used are words ofdescription rather than limitation and that changes within the purviewof the appended claims may be made without departing from the true scopeand spirit of the invention in its broader aspects.

1. A method for creating based on design data a template that is usedfor image recognition by an imaging apparatus, wherein luminanceinformation is set for each area in the template based on the materialinformation of the region defined by the template.
 2. The method forcreating a template according to claim 1, wherein the imaging apparatusis an optical microscope; and wherein the luminance information is setfor each area in the template based on at least one of information fromamong the material information, the pattern size information of apattern arranged in the region defined by the template, and the layerinformation of the region defined by the template.
 3. The method forcreating a template according to claim 1, wherein the imaging apparatusis a scanning electron microscope; and wherein the luminance informationis set for each area in the template based on the material information,the setup conditions of the scanning electron microscope, and thepattern outline information of a pattern arranged in the region definedby the template.
 4. An image processing apparatus comprising: a storagemedium for storing design data of a semiconductor device; and a templatecreation unit for creating, based on part of the design data stored onthe storage medium, a template that is used to recognize a particulararea in a scanning electron microscope image, wherein the templatecreation unit sets luminance information for each area in the templatein accordance with the emission amount information of secondaryelectrons that is stored for each material constituting the particulararea in the scanning electron microscope image.